Social network data such as data derived from blogs, reviews, message boards, etc., typically include anonymous entries that are based on the subjective opinion of the author and/or consumer. Additionally, such data presented can be contradictory and it may be left to the user to derive meaningful conclusions therefrom. Accordingly, subjectivity can be an important and challenging factor for recommendation systems.
Existing recommendation systems and approaches commonly utilize metrics that rely on analysis of social network data, overlooking the problem of anonymity, and merely provide quantitative measures that summarize opinions presented within the social network data.
Consequently, a need exists for a metric to elicit an importance level of a given item of content with respect to an individual while masking the actual identity of the content owner.